Overview

Dataset statistics

Number of variables44
Number of observations2374
Missing cells3120
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory699.2 KiB
Average record size in memory301.6 B

Variable types

NUM19
CAT17
BOOL8

Warnings

City_Name has a high cardinality: 168 distinct values High cardinality
ZIP has a high cardinality: 359 distinct values High cardinality
All_lengths is highly correlated with num_bikelanes_Cls2High correlation
num_bikelanes_Cls2 is highly correlated with All_lengthsHigh correlation
Cnty_Name is highly correlated with COUNTYFP10High correlation
COUNTYFP10 is highly correlated with Cnty_NameHigh correlation
Disadv_Neighborhds1990_2015 is highly correlated with Disadv_Neighborhds1990_2000 and 1 other fieldsHigh correlation
Disadv_Neighborhds1990_2000 is highly correlated with Disadv_Neighborhds1990_2015High correlation
Disadv_Neighborhds2000_2015 is highly correlated with Disadv_Neighborhds1990_2015High correlation
Gentrified_1990_2015 is highly correlated with Gentrified_1990_2000 and 1 other fieldsHigh correlation
Gentrified_1990_2000 is highly correlated with Gentrified_1990_2015High correlation
Gentrified_2000_2015 is highly correlated with Gentrified_1990_2015High correlation
Cnty_Name has 61 (2.6%) missing values Missing
City_Name has 70 (2.9%) missing values Missing
Chg_median_rent2000_2015 has 47 (2.0%) missing values Missing
Chg_median_fam_inc2000_2015 has 27 (1.1%) missing values Missing
Disadv_Neighborhds1990_2015 has 1358 (57.2%) missing values Missing
Gentrified_1990_2015 has 1358 (57.2%) missing values Missing
num_bikelanes_Cls1 is highly skewed (γ1 = 21.63996336) Skewed
sqmi is highly skewed (γ1 = 20.16372471) Skewed
GEOID10 has unique values Unique
num_bikelanes_Cls1 has 2334 (98.3%) zeros Zeros
num_bikelanes_Cls2 has 1923 (81.0%) zeros Zeros
num_bikelanes_Cls3 has 2130 (89.7%) zeros Zeros
All_lengths has 1806 (76.1%) zeros Zeros
Chg_Sec8_fam2000_2015 has 155 (6.5%) zeros Zeros

Reproduction

Analysis started2020-10-25 05:36:41.157897
Analysis finished2020-10-25 05:37:40.542590
Duration59.38 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

GEOID10
Categorical

UNIQUE

Distinct2374
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size103.2 KiB
6059980000
 
1
6037302102
 
1
6037302003
 
1
6037302002
 
1
6037301900
 
1
Other values (2369)
2369 
ValueCountFrequency (%) 
60599800001< 0.1%
 
60373021021< 0.1%
 
60373020031< 0.1%
 
60373020021< 0.1%
 
60373019001< 0.1%
 
60373018021< 0.1%
 
60373018011< 0.1%
 
60373016021< 0.1%
 
60373015021< 0.1%
 
60373015011< 0.1%
 
Other values (2364)236499.6%
 
2020-10-24T22:37:40.703730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2374 ?
Unique (%)100.0%
2020-10-24T22:37:40.815562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

COUNTYFP10
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
37
1857 
59
517 
ValueCountFrequency (%) 
37185778.2%
 
5951721.8%
 
2020-10-24T22:37:40.915421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:40.984503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:41.053668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Cnty_Name
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing61
Missing (%)2.6%
Memory size2.4 KiB
Los Angeles
1802 
Orange
511 
ValueCountFrequency (%) 
Los Angeles180275.9%
 
Orange51121.5%
 
(Missing)612.6%
 
2020-10-24T22:37:41.152357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:41.220556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:41.312701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length9.718197136
Min length3

City_Name
Categorical

HIGH CARDINALITY
MISSING

Distinct168
Distinct (%)7.3%
Missing70
Missing (%)2.9%
Memory size10.9 KiB
Los Angeles
773 
Long Beach
 
94
Anaheim
 
48
Santa Ana
 
46
Huntington Beach
 
42
Other values (163)
1301 
ValueCountFrequency (%) 
Los Angeles77332.6%
 
Long Beach944.0%
 
Anaheim482.0%
 
Santa Ana461.9%
 
Huntington Beach421.8%
 
Garden Grove331.4%
 
Glendale321.3%
 
Torrance311.3%
 
Santa Clarita291.2%
 
Irvine251.1%
 
Other values (158)115148.5%
 
(Missing)702.9%
 
2020-10-24T22:37:41.458892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique24 ?
Unique (%)1.0%
2020-10-24T22:37:41.592947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length11
Mean length10.24557709
Min length3

ZIP
Categorical

HIGH CARDINALITY

Distinct359
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
90044
 
24
90201
 
22
90255
 
22
90026
 
21
90280
 
20
Other values (354)
2265 
ValueCountFrequency (%) 
90044241.0%
 
90201220.9%
 
90255220.9%
 
90026210.9%
 
90280200.8%
 
91331190.8%
 
90011190.8%
 
90706180.8%
 
90805180.8%
 
90650180.8%
 
Other values (349)217391.5%
 
2020-10-24T22:37:41.723152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique34 ?
Unique (%)1.4%
2020-10-24T22:37:41.835808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
0
2373 
262.8681813
 
1
ValueCountFrequency (%) 
02373> 99.9%
 
262.86818131< 0.1%
 
2020-10-24T22:37:41.939024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-24T22:37:42.001177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:42.078279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length3.00336984
Min length3

num_bikelanes_Cls1
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct41
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.65581466
Minimum0
Maximum7412.502826
Zeros2334
Zeros (%)98.3%
Memory size18.5 KiB
2020-10-24T22:37:42.191909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7412.502826
Range7412.502826
Interquartile range (IQR)0

Descriptive statistics

Standard deviation222.8532228
Coefficient of variation (CV)12.62208667
Kurtosis604.6432204
Mean17.65581466
Median Absolute Deviation (MAD)0
Skewness21.63996336
Sum41914.90399
Variance49663.55893
MonotocityNot monotonic
2020-10-24T22:37:42.309330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%) 
0233498.3%
 
898.93211971< 0.1%
 
0.17703521< 0.1%
 
2054.7916841< 0.1%
 
1116.7218721< 0.1%
 
1097.6200061< 0.1%
 
782.87022491< 0.1%
 
1921.6835361< 0.1%
 
2099.202561< 0.1%
 
204.63183441< 0.1%
 
Other values (31)311.3%
 
ValueCountFrequency (%) 
0233498.3%
 
0.1421148741< 0.1%
 
0.17703521< 0.1%
 
0.2263385981< 0.1%
 
0.2593010411< 0.1%
 
ValueCountFrequency (%) 
7412.5028261< 0.1%
 
4589.8742571< 0.1%
 
2191.771151< 0.1%
 
2099.202561< 0.1%
 
2054.7916841< 0.1%
 

num_bikelanes_Cls2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct452
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.7092936
Minimum0
Maximum9384.668542
Zeros1923
Zeros (%)81.0%
Memory size18.5 KiB
2020-10-24T22:37:42.828908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1644.214016
Maximum9384.668542
Range9384.668542
Interquartile range (IQR)0

Descriptive statistics

Standard deviation771.4150573
Coefficient of variation (CV)3.191499366
Kurtosis38.34917837
Mean241.7092936
Median Absolute Deviation (MAD)0
Skewness5.268196888
Sum573817.863
Variance595081.1907
MonotocityNot monotonic
2020-10-24T22:37:42.960326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0192381.0%
 
1386.6576521< 0.1%
 
1590.461031< 0.1%
 
611.61154791< 0.1%
 
8171.3920731< 0.1%
 
913.16160971< 0.1%
 
3513.9939321< 0.1%
 
305.43780841< 0.1%
 
1947.8706381< 0.1%
 
2777.5210571< 0.1%
 
Other values (442)44218.6%
 
ValueCountFrequency (%) 
0192381.0%
 
0.0613670151< 0.1%
 
0.2495046621< 0.1%
 
0.3231604831< 0.1%
 
0.3427011321< 0.1%
 
ValueCountFrequency (%) 
9384.6685421< 0.1%
 
8783.7367091< 0.1%
 
8631.2438851< 0.1%
 
8171.3920731< 0.1%
 
6640.9042631< 0.1%
 

num_bikelanes_Cls3
Real number (ℝ≥0)

ZEROS

Distinct245
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.82468164
Minimum0
Maximum6103.364354
Zeros2130
Zeros (%)89.7%
Memory size18.5 KiB
2020-10-24T22:37:43.096889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile631.3934306
Maximum6103.364354
Range6103.364354
Interquartile range (IQR)0

Descriptive statistics

Standard deviation310.062506
Coefficient of variation (CV)4.089202873
Kurtosis79.10244789
Mean75.82468164
Median Absolute Deviation (MAD)0
Skewness6.860138028
Sum180007.7942
Variance96138.75763
MonotocityNot monotonic
2020-10-24T22:37:43.221272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0213089.7%
 
417.91745391< 0.1%
 
1497.7202381< 0.1%
 
382.66815321< 0.1%
 
606.1616061< 0.1%
 
248.55745581< 0.1%
 
1704.8803191< 0.1%
 
1308.3076591< 0.1%
 
13.687354241< 0.1%
 
830.3802131< 0.1%
 
Other values (235)2359.9%
 
ValueCountFrequency (%) 
0213089.7%
 
0.3655142721< 0.1%
 
0.4049379561< 0.1%
 
0.675272781< 0.1%
 
0.6859265861< 0.1%
 
ValueCountFrequency (%) 
6103.3643541< 0.1%
 
3038.1970361< 0.1%
 
2555.4530731< 0.1%
 
2384.0469311< 0.1%
 
2290.5748951< 0.1%
 
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
0
2371 
1344.616251
 
1
603.8689225
 
1
1730.604259
 
1
ValueCountFrequency (%) 
0237199.9%
 
1344.6162511< 0.1%
 
603.86892251< 0.1%
 
1730.6042591< 0.1%
 
2020-10-24T22:37:43.342432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.1%
2020-10-24T22:37:43.409586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:43.498459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length3
Mean length3.0126369
Min length3

All_lengths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct569
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.8502607
Minimum0
Maximum22147.11107
Zeros1806
Zeros (%)76.1%
Memory size18.5 KiB
2020-10-24T22:37:43.613610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2019.292489
Maximum22147.11107
Range22147.11107
Interquartile range (IQR)0

Descriptive statistics

Standard deviation986.4215266
Coefficient of variation (CV)2.928368007
Kurtosis113.617807
Mean336.8502607
Median Absolute Deviation (MAD)0
Skewness7.419112326
Sum799682.5188
Variance973027.4282
MonotocityNot monotonic
2020-10-24T22:37:43.742848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0180676.1%
 
215.35604921< 0.1%
 
2924.8315871< 0.1%
 
6403.3430171< 0.1%
 
281.45673511< 0.1%
 
13.946655281< 0.1%
 
141.72227521< 0.1%
 
35.836172761< 0.1%
 
18.515412481< 0.1%
 
1022.4692221< 0.1%
 
Other values (559)55923.5%
 
ValueCountFrequency (%) 
0180676.1%
 
0.1421148741< 0.1%
 
0.6119951991< 0.1%
 
0.6343899821< 0.1%
 
0.652828991< 0.1%
 
ValueCountFrequency (%) 
22147.111071< 0.1%
 
9384.6685421< 0.1%
 
8783.7367091< 0.1%
 
8171.3920731< 0.1%
 
6640.9042631< 0.1%
 

sqmi
Real number (ℝ≥0)

SKEWED

Distinct2373
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.722624136
Minimum0.02701094
Maximum397.2516
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:43.880141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.02701094
5-th percentile0.124173885
Q10.251679475
median0.45106075
Q30.7582048
95-th percentile2.62004485
Maximum397.2516
Range397.2245891
Interquartile range (IQR)0.506525325

Descriptive statistics

Standard deviation12.80269913
Coefficient of variation (CV)7.432090878
Kurtosis500.1991974
Mean1.722624136
Median Absolute Deviation (MAD)0.22486185
Skewness20.16372471
Sum4089.509699
Variance163.9091049
MonotocityNot monotonic
2020-10-24T22:37:44.010604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.381954320.1%
 
0.21161951< 0.1%
 
0.61680361< 0.1%
 
0.23809261< 0.1%
 
0.50066871< 0.1%
 
2.6150811< 0.1%
 
0.39243311< 0.1%
 
0.57235251< 0.1%
 
0.27034291< 0.1%
 
0.42746961< 0.1%
 
Other values (2363)236399.5%
 
ValueCountFrequency (%) 
0.027010941< 0.1%
 
0.030732191< 0.1%
 
0.035866191< 0.1%
 
0.035888581< 0.1%
 
0.041048841< 0.1%
 
ValueCountFrequency (%) 
397.25161< 0.1%
 
237.7371< 0.1%
 
227.17371< 0.1%
 
170.11761< 0.1%
 
144.10251< 0.1%
 

Chg_median_rent2000_2015
Real number (ℝ)

MISSING

Distinct2321
Distinct (%)99.7%
Missing47
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean285.3333735
Minimum-1462.945455
Maximum3228.697189
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:44.144079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1462.945455
5-th percentile-5.981343874
Q1162.6158103
median246.9312253
Q3356.0470356
95-th percentile727.1345207
Maximum3228.697189
Range4691.642644
Interquartile range (IQR)193.4312253

Descriptive statistics

Standard deviation274.6654764
Coefficient of variation (CV)0.9626125157
Kurtosis14.69416253
Mean285.3333735
Median Absolute Deviation (MAD)93.8347826
Skewness1.75470523
Sum663970.7601
Variance75441.12395
MonotocityNot monotonic
2020-10-24T22:37:44.274740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
746.054545530.1%
 
299.307509920.1%
 
330.863241120.1%
 
169.037154220.1%
 
243.751778720.1%
 
205.85259431< 0.1%
 
-724.94782611< 0.1%
 
223.69328061< 0.1%
 
259.61814761< 0.1%
 
271.23427431< 0.1%
 
Other values (2311)231197.3%
 
(Missing)472.0%
 
ValueCountFrequency (%) 
-1462.9454551< 0.1%
 
-1317.0783621< 0.1%
 
-1307.9454551< 0.1%
 
-779.02587481< 0.1%
 
-748.07509881< 0.1%
 
ValueCountFrequency (%) 
3228.6971891< 0.1%
 
2199.8948621< 0.1%
 
2143.1581031< 0.1%
 
1951.6798421< 0.1%
 
1889.8181821< 0.1%
 

Chg_median_rent1990_2000
Real number (ℝ)

Distinct2355
Distinct (%)99.4%
Missing5
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean-0.1038988223
Minimum-0.421195165
Maximum0.34200552
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:44.403677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.421195165
5-th percentile-0.2659549834
Q1-0.166949445
median-0.086447535
Q3-0.032776865
95-th percentile0.00380754
Maximum0.34200552
Range0.763200685
Interquartile range (IQR)0.13417258

Descriptive statistics

Standard deviation0.09035291878
Coefficient of variation (CV)-0.8696240896
Kurtosis0.3427527997
Mean-0.1038988223
Median Absolute Deviation (MAD)0.062214354
Skewness-0.4599002598
Sum-246.1363101
Variance0.008163649931
MonotocityNot monotonic
2020-10-24T22:37:44.530583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.1046908620.1%
 
-0.01702084220.1%
 
0.00180478920.1%
 
-0.00731824920.1%
 
-0.22141375420.1%
 
-0.24121734220.1%
 
-0.0707557320.1%
 
-0.17316254720.1%
 
0.00287655320.1%
 
-0.17066758920.1%
 
Other values (2345)234998.9%
 
(Missing)50.2%
 
ValueCountFrequency (%) 
-0.4211951651< 0.1%
 
-0.4185490611< 0.1%
 
-0.4048686491< 0.1%
 
-0.3958813971< 0.1%
 
-0.3860919061< 0.1%
 
ValueCountFrequency (%) 
0.342005521< 0.1%
 
0.3420055091< 0.1%
 
0.3420054871< 0.1%
 
0.1450362421< 0.1%
 
0.1198809941< 0.1%
 
Distinct2362
Distinct (%)100.0%
Missing12
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean-0.05926072944
Minimum-0.736516016
Maximum0.944389484
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:44.668968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.736516016
5-th percentile-0.2033859735
Q1-0.1112246433
median-0.049011992
Q3-0.00633424175
95-th percentile0.06418931275
Maximum0.944389484
Range1.6809055
Interquartile range (IQR)0.1048904015

Descriptive statistics

Standard deviation0.08929509198
Coefficient of variation (CV)-1.506817294
Kurtosis9.969345907
Mean-0.05926072944
Median Absolute Deviation (MAD)0.0489800765
Skewness0.05214090302
Sum-139.9738429
Variance0.007973613452
MonotocityNot monotonic
2020-10-24T22:37:44.797660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.1324822671< 0.1%
 
-0.1808616651< 0.1%
 
-0.1101273681< 0.1%
 
-0.0752215851< 0.1%
 
0.0751595341< 0.1%
 
-0.0185881031< 0.1%
 
-0.1516139791< 0.1%
 
-0.1043015331< 0.1%
 
-0.0285546491< 0.1%
 
-0.1795910951< 0.1%
 
Other values (2352)235299.1%
 
(Missing)120.5%
 
ValueCountFrequency (%) 
-0.7365160161< 0.1%
 
-0.6321352481< 0.1%
 
-0.5731554311< 0.1%
 
-0.5719815451< 0.1%
 
-0.4197156721< 0.1%
 
ValueCountFrequency (%) 
0.9443894841< 0.1%
 
0.2992860651< 0.1%
 
0.2542628911< 0.1%
 
0.2461306651< 0.1%
 
0.230843361< 0.1%
 
Distinct2222
Distinct (%)93.8%
Missing5
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.006801124676
Minimum-0.373453318
Maximum0.302114054
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:44.933351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.373453318
5-th percentile-0.0776895016
Q1-0.031092733
median-0.00248485
Q30.038600939
95-th percentile0.1177888448
Maximum0.302114054
Range0.675567372
Interquartile range (IQR)0.069693672

Descriptive statistics

Standard deviation0.06392870255
Coefficient of variation (CV)9.399725133
Kurtosis3.469222844
Mean0.006801124676
Median Absolute Deviation (MAD)0.033699691
Skewness0.5698631194
Sum16.11186436
Variance0.00408687901
MonotocityNot monotonic
2020-10-24T22:37:45.067530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.01422654240.2%
 
-0.01520731130.1%
 
-0.00358735430.1%
 
0.02392261530.1%
 
-0.37345331830.1%
 
0.06404318130.1%
 
-0.05604147530.1%
 
0.16718737520.1%
 
0.24274789520.1%
 
0.10049908720.1%
 
Other values (2212)234198.6%
 
(Missing)50.2%
 
ValueCountFrequency (%) 
-0.37345331830.1%
 
-0.2262942681< 0.1%
 
-0.1931554991< 0.1%
 
-0.1809830121< 0.1%
 
-0.1732850111< 0.1%
 
ValueCountFrequency (%) 
0.3021140541< 0.1%
 
0.2884740861< 0.1%
 
0.2851094891< 0.1%
 
0.2823151141< 0.1%
 
0.27763378320.1%
 
Distinct2359
Distinct (%)100.0%
Missing15
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean-0.07084597051
Minimum-0.386744052
Maximum0.889041804
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:45.204838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.386744052
5-th percentile-0.2139461301
Q1-0.1191905965
median-0.054313295
Q3-0.0157077095
95-th percentile0.022477357
Maximum0.889041804
Range1.275785856
Interquartile range (IQR)0.103482887

Descriptive statistics

Standard deviation0.07719384492
Coefficient of variation (CV)-1.089601065
Kurtosis10.28009511
Mean-0.07084597051
Median Absolute Deviation (MAD)0.046476377
Skewness0.1103818248
Sum-167.1256444
Variance0.005958889693
MonotocityNot monotonic
2020-10-24T22:37:45.339155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.1415190471< 0.1%
 
-0.093993261< 0.1%
 
-0.1260827771< 0.1%
 
-0.0813475541< 0.1%
 
0.0068489811< 0.1%
 
-0.046529861< 0.1%
 
-0.0350803121< 0.1%
 
-0.2464735381< 0.1%
 
-0.1466880671< 0.1%
 
-0.2071220431< 0.1%
 
Other values (2349)234998.9%
 
(Missing)150.6%
 
ValueCountFrequency (%) 
-0.3867440521< 0.1%
 
-0.3833703591< 0.1%
 
-0.3562759451< 0.1%
 
-0.3500089351< 0.1%
 
-0.3492447981< 0.1%
 
ValueCountFrequency (%) 
0.8890418041< 0.1%
 
0.1599351141< 0.1%
 
0.1597883131< 0.1%
 
0.1591490141< 0.1%
 
0.1420424261< 0.1%
 
Distinct2222
Distinct (%)93.8%
Missing5
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.02568868968
Minimum-0.43125
Maximum0.614975376
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:45.473050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.43125
5-th percentile-0.055007462
Q1-0.009483572
median0.015701152
Q30.055371409
95-th percentile0.1309368336
Maximum0.614975376
Range1.046225376
Interquartile range (IQR)0.064854981

Descriptive statistics

Standard deviation0.05851240325
Coefficient of variation (CV)2.277749624
Kurtosis7.538906152
Mean0.02568868968
Median Absolute Deviation (MAD)0.031011231
Skewness0.9366621611
Sum60.85650586
Variance0.003423701334
MonotocityNot monotonic
2020-10-24T22:37:45.608627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.14217410940.2%
 
0.01584048130.1%
 
0.15993493230.1%
 
-0.03027111130.1%
 
-0.00514094130.1%
 
0.17407138630.1%
 
0.00371485630.1%
 
-0.00835126320.1%
 
-0.02389000320.1%
 
-0.03719313620.1%
 
Other values (2212)234198.6%
 
(Missing)50.2%
 
ValueCountFrequency (%) 
-0.431251< 0.1%
 
-0.1710167021< 0.1%
 
-0.1654106981< 0.1%
 
-0.1650182571< 0.1%
 
-0.1439803521< 0.1%
 
ValueCountFrequency (%) 
0.6149753761< 0.1%
 
0.4143006711< 0.1%
 
0.3984142071< 0.1%
 
0.254380241< 0.1%
 
0.2521368081< 0.1%
 
Distinct2359
Distinct (%)100.0%
Missing15
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.05651047063
Minimum-0.553274974
Maximum0.503626595
Zeros1
Zeros (%)< 0.1%
Memory size18.5 KiB
2020-10-24T22:37:45.744808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.553274974
5-th percentile-0.0308119679
Q10.0143398095
median0.048624049
Q30.0927211915
95-th percentile0.1680142576
Maximum0.503626595
Range1.056901569
Interquartile range (IQR)0.078381382

Descriptive statistics

Standard deviation0.06756582217
Coefficient of variation (CV)1.195633684
Kurtosis8.028309215
Mean0.05651047063
Median Absolute Deviation (MAD)0.038033919
Skewness0.3146336811
Sum133.3082002
Variance0.004565140326
MonotocityNot monotonic
2020-10-24T22:37:45.882142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0486000121< 0.1%
 
0.0424714391< 0.1%
 
0.0467320631< 0.1%
 
0.0639151361< 0.1%
 
0.0522668611< 0.1%
 
0.0226902971< 0.1%
 
0.0887024581< 0.1%
 
0.1333059431< 0.1%
 
0.0811610621< 0.1%
 
-0.0259756821< 0.1%
 
Other values (2349)234998.9%
 
(Missing)150.6%
 
ValueCountFrequency (%) 
-0.5532749741< 0.1%
 
-0.4863241521< 0.1%
 
-0.3424470421< 0.1%
 
-0.1856552311< 0.1%
 
-0.1807417051< 0.1%
 
ValueCountFrequency (%) 
0.5036265951< 0.1%
 
0.480956081< 0.1%
 
0.4797138171< 0.1%
 
0.4169199461< 0.1%
 
0.3902332761< 0.1%
 
Distinct2365
Distinct (%)> 99.9%
Missing8
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean-2478.976848
Minimum-59081.96745
Maximum99124.59293
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:46.019591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-59081.96745
5-th percentile-19324.76933
Q1-8683.633738
median-3430.160713
Q32343.38126
95-th percentile16125.79762
Maximum99124.59293
Range158206.5604
Interquartile range (IQR)11027.015

Descriptive statistics

Standard deviation12617.82342
Coefficient of variation (CV)-5.089931934
Kurtosis9.542619737
Mean-2478.976848
Median Absolute Deviation (MAD)5532.347776
Skewness1.552152118
Sum-5865259.221
Variance159209467.8
MonotocityNot monotonic
2020-10-24T22:37:46.153162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-14596.6365520.1%
 
-7363.1769971< 0.1%
 
4300.6683161< 0.1%
 
-4235.592131< 0.1%
 
-4138.0169651< 0.1%
 
4957.5879131< 0.1%
 
-9144.1569031< 0.1%
 
2101.2051381< 0.1%
 
9306.4723451< 0.1%
 
1622.7026641< 0.1%
 
Other values (2355)235599.2%
 
(Missing)80.3%
 
ValueCountFrequency (%) 
-59081.967451< 0.1%
 
-55299.541681< 0.1%
 
-52558.040191< 0.1%
 
-49365.070791< 0.1%
 
-49271.531571< 0.1%
 
ValueCountFrequency (%) 
99124.592931< 0.1%
 
82657.745681< 0.1%
 
79822.160851< 0.1%
 
74928.303061< 0.1%
 
74664.146271< 0.1%
 

Chg_median_fam_inc2000_2015
Real number (ℝ)

MISSING

Distinct2347
Distinct (%)100.0%
Missing27
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean-5788.661435
Minimum-120638.8184
Maximum69488.85172
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:46.303019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-120638.8184
5-th percentile-26613.46246
Q1-11896.99937
median-4920.256699
Q31428.415816
95-th percentile13382.5462
Maximum69488.85172
Range190127.6701
Interquartile range (IQR)13325.41519

Descriptive statistics

Standard deviation13711.1537
Coefficient of variation (CV)-2.368622496
Kurtosis7.875859389
Mean-5788.661435
Median Absolute Deviation (MAD)6646.504621
Skewness-1.111816564
Sum-13585988.39
Variance187995735.7
MonotocityNot monotonic
2020-10-24T22:37:46.436411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-9878.2646951< 0.1%
 
-9293.2856561< 0.1%
 
-7468.7738491< 0.1%
 
-1807.4307321< 0.1%
 
1719.3155321< 0.1%
 
8302.1189211< 0.1%
 
-14642.438861< 0.1%
 
-9098.4032371< 0.1%
 
5108.7422861< 0.1%
 
-5593.2825651< 0.1%
 
Other values (2337)233798.4%
 
(Missing)271.1%
 
ValueCountFrequency (%) 
-120638.81841< 0.1%
 
-104942.20981< 0.1%
 
-98582.518851< 0.1%
 
-94993.092841< 0.1%
 
-80190.378421< 0.1%
 
ValueCountFrequency (%) 
69488.851721< 0.1%
 
55149.224241< 0.1%
 
52550.760111< 0.1%
 
47532.823871< 0.1%
 
47178.630311< 0.1%
 
Distinct2217
Distinct (%)93.8%
Missing10
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean-0.0355145496
Minimum-0.602230762
Maximum0.619618237
Zeros1
Zeros (%)< 0.1%
Memory size18.5 KiB
2020-10-24T22:37:46.576232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.602230762
5-th percentile-0.2050034245
Q1-0.090895612
median-0.0358443975
Q30.023867631
95-th percentile0.1259772165
Maximum0.619618237
Range1.221848999
Interquartile range (IQR)0.114763243

Descriptive statistics

Standard deviation0.1074663284
Coefficient of variation (CV)-3.025980327
Kurtosis4.013476858
Mean-0.0355145496
Median Absolute Deviation (MAD)0.0571560875
Skewness-0.1026694376
Sum-83.95639526
Variance0.01154901175
MonotocityNot monotonic
2020-10-24T22:37:46.727110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.20683883340.2%
 
-0.05958736130.1%
 
-0.24340817430.1%
 
-0.02587459130.1%
 
-0.24246424630.1%
 
-0.02638374130.1%
 
0.02360269430.1%
 
-0.11175463320.1%
 
-0.03728155820.1%
 
-0.05921193620.1%
 
Other values (2207)233698.4%
 
(Missing)100.4%
 
ValueCountFrequency (%) 
-0.6022307621< 0.1%
 
-0.5966735971< 0.1%
 
-0.5580645161< 0.1%
 
-0.5163693131< 0.1%
 
-0.5104311541< 0.1%
 
ValueCountFrequency (%) 
0.6196182371< 0.1%
 
0.5833333331< 0.1%
 
0.5631900421< 0.1%
 
0.4372918981< 0.1%
 
0.39139784920.1%
 
Distinct2352
Distinct (%)100.0%
Missing22
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.1298602903
Minimum-0.8125
Maximum0.836543339
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:46.866104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8125
5-th percentile-0.0924324288
Q10.05417583975
median0.135275396
Q30.208379902
95-th percentile0.3401752597
Maximum0.836543339
Range1.649043339
Interquartile range (IQR)0.1542040622

Descriptive statistics

Standard deviation0.139429799
Coefficient of variation (CV)1.0736908
Kurtosis3.373545177
Mean0.1298602903
Median Absolute Deviation (MAD)0.0777278535
Skewness-0.3218403246
Sum305.4314028
Variance0.01944066884
MonotocityNot monotonic
2020-10-24T22:37:46.991543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0279372771< 0.1%
 
0.1425340081< 0.1%
 
-0.0873980991< 0.1%
 
0.2424196481< 0.1%
 
0.1379685061< 0.1%
 
0.1999356211< 0.1%
 
-0.0454545451< 0.1%
 
0.2051477451< 0.1%
 
0.2196343931< 0.1%
 
0.0760505551< 0.1%
 
Other values (2342)234298.7%
 
(Missing)220.9%
 
ValueCountFrequency (%) 
-0.81251< 0.1%
 
-0.684328191< 0.1%
 
-0.6428571431< 0.1%
 
-0.4842105261< 0.1%
 
-0.4651933811< 0.1%
 
ValueCountFrequency (%) 
0.8365433391< 0.1%
 
0.8066298341< 0.1%
 
0.7867768251< 0.1%
 
0.6693626271< 0.1%
 
0.6466165411< 0.1%
 

Chg_median_rent_1990_2000
Real number (ℝ)

Distinct2184
Distinct (%)92.4%
Missing10
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean-61.54477675
Minimum-1611.202255
Maximum1881.841736
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:47.131262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1611.202255
5-th percentile-303.4801786
Q1-182.3792444
median-115.0598313
Q3-22.12938721
95-th percentile446.3298254
Maximum1881.841736
Range3493.043991
Interquartile range (IQR)160.2498572

Descriptive statistics

Standard deviation260.3993186
Coefficient of variation (CV)-4.231054727
Kurtosis8.873046965
Mean-61.54477675
Median Absolute Deviation (MAD)76.64708375
Skewness2.080503856
Sum-145491.8522
Variance67807.80512
MonotocityNot monotonic
2020-10-24T22:37:47.261840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
992.711498170.7%
 
-140.771867540.2%
 
-158.714334530.1%
 
122.899640330.1%
 
198.372054830.1%
 
-75.2853399430.1%
 
-186.06129730.1%
 
-134.426632230.1%
 
-118.904311930.1%
 
-113.788622430.1%
 
Other values (2174)231997.7%
 
(Missing)100.4%
 
ValueCountFrequency (%) 
-1611.2022551< 0.1%
 
-1478.6288871< 0.1%
 
-1186.8247281< 0.1%
 
-1053.3445191< 0.1%
 
-913.57339581< 0.1%
 
ValueCountFrequency (%) 
1881.8417361< 0.1%
 
1343.230111< 0.1%
 
1247.4968411< 0.1%
 
1234.8391841< 0.1%
 
1202.6958961< 0.1%
 

Chg_Sec8_fam2000_2015
Real number (ℝ)

ZEROS

Distinct2196
Distinct (%)93.2%
Missing19
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean0.006289417134
Minimum-0.13206533
Maximum0.502762431
Zeros155
Zeros (%)6.5%
Memory size18.5 KiB
2020-10-24T22:37:47.396438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.13206533
5-th percentile-0.0139040324
Q1-0.001852601
median0.000439871
Q30.0086532095
95-th percentile0.0435641636
Maximum0.502762431
Range0.634827761
Interquartile range (IQR)0.0105058105

Descriptive statistics

Standard deviation0.0224955084
Coefficient of variation (CV)3.576723871
Kurtosis108.475653
Mean0.006289417134
Median Absolute Deviation (MAD)0.004155162
Skewness6.23822485
Sum14.81157735
Variance0.0005060478982
MonotocityNot monotonic
2020-10-24T22:37:47.529864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01556.5%
 
-0.00677743820.1%
 
-1.64e-0620.1%
 
0.00167879120.1%
 
-2.09e-0520.1%
 
-0.0004933420.1%
 
0.053616091< 0.1%
 
0.0043896461< 0.1%
 
-0.002609621< 0.1%
 
0.0003038641< 0.1%
 
Other values (2186)218692.1%
 
(Missing)190.8%
 
ValueCountFrequency (%) 
-0.132065331< 0.1%
 
-0.0938852211< 0.1%
 
-0.0605641841< 0.1%
 
-0.0604736261< 0.1%
 
-0.0524843881< 0.1%
 
ValueCountFrequency (%) 
0.5027624311< 0.1%
 
0.176793591< 0.1%
 
0.1598262211< 0.1%
 
0.1591051971< 0.1%
 
0.1540250171< 0.1%
 
Distinct5
Distinct (%)0.2%
Missing16
Missing (%)0.7%
Memory size2.5 KiB
01. Majority NHW
1252 
03. Majority Hisp
583 
05. No Majority
367 
02. Majority Black
138 
04. Majority Asian
 
18
ValueCountFrequency (%) 
01. Majority NHW125252.7%
 
03. Majority Hisp58324.6%
 
05. No Majority36715.5%
 
02. Majority Black1385.8%
 
04. Majority Asian180.8%
 
(Missing)160.7%
 
2020-10-24T22:37:47.666479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:47.754484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:47.859235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length16
Mean length16.1347936
Min length3
Distinct5
Distinct (%)0.2%
Missing15
Missing (%)0.6%
Memory size2.5 KiB
01. Majority NHW
898 
03. Majority Hisp
822 
05. No Majority
493 
04. Majority Asian
 
76
02. Majority Black
 
70
ValueCountFrequency (%) 
01. Majority NHW89837.8%
 
03. Majority Hisp82234.6%
 
05. No Majority49320.8%
 
04. Majority Asian763.2%
 
02. Majority Black702.9%
 
(Missing)150.6%
 
2020-10-24T22:37:47.968870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:48.042449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:48.148387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length16
Mean length16.17944398
Min length3
Distinct5
Distinct (%)0.2%
Missing18
Missing (%)0.8%
Memory size2.5 KiB
03. Majority Hisp
952 
01. Majority NHW
743 
05. No Majority
489 
04. Majority Asian
126 
02. Majority Black
 
46
ValueCountFrequency (%) 
03. Majority Hisp95240.1%
 
01. Majority NHW74331.3%
 
05. No Majority48920.6%
 
04. Majority Asian1265.3%
 
02. Majority Black461.9%
 
(Missing)180.8%
 
2020-10-24T22:37:48.260574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:48.331531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:48.435374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length16
Mean length16.24136479
Min length3

Disadv_Neighborhds1990_2000
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.1%
Memory size2.4 KiB
0
1429 
1
942 
ValueCountFrequency (%) 
0142960.2%
 
194239.7%
 
(Missing)30.1%
 
2020-10-24T22:37:48.537937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:48.605494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:48.673049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Disadv_Neighborhds2000_2015
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.1%
Memory size2.4 KiB
0
1459 
1
912 
ValueCountFrequency (%) 
0145961.5%
 
191238.4%
 
(Missing)30.1%
 
2020-10-24T22:37:48.768852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:48.837915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:48.905372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Gentrified_1990_2000
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.1%
Memory size2.4 KiB
0
2310 
1
 
61
ValueCountFrequency (%) 
0231097.3%
 
1612.6%
 
(Missing)30.1%
 
2020-10-24T22:37:48.999929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:49.061548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:49.129096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Gentrified_2000_2015
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.1%
Memory size2.4 KiB
0
2307 
1
 
64
ValueCountFrequency (%) 
0230797.2%
 
1642.7%
 
(Missing)30.1%
 
2020-10-24T22:37:49.221987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:49.286884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:49.353872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Disadv_Neighborhds1990_2015
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.3%
Missing1358
Missing (%)57.2%
Memory size2.4 KiB
Disadvantaged Both Decades
838 
Disadvantaged 1990-2000 Only
104 
Disadvantaged 2000-2015 Only
 
74
ValueCountFrequency (%) 
Disadvantaged Both Decades83835.3%
 
Disadvantaged 1990-2000 Only1044.4%
 
Disadvantaged 2000-2015 Only743.1%
 
(Missing)135857.2%
 
2020-10-24T22:37:49.459642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:49.537528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:49.631066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length3
Mean length12.99326032
Min length3

Gentrified_1990_2015
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.4%
Missing1358
Missing (%)57.2%
Memory size2.5 KiB
Disadvantaged, Did Not Gentrify
896 
Gentrified 2000-2015 Only
 
59
Gentrified 1990-2000 Only
 
56
Gentrified Both Decades
 
5
ValueCountFrequency (%) 
Disadvantaged, Did Not Gentrify89637.7%
 
Gentrified 2000-2015 Only592.5%
 
Gentrified 1990-2000 Only562.4%
 
Gentrified Both Decades50.2%
 
(Missing)135857.2%
 
2020-10-24T22:37:49.733290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:49.799984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:49.899679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length3
Mean length14.67565291
Min length3

Job_density_2015_jobs_sqmi
Real number (ℝ≥0)

Distinct2372
Distinct (%)100.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3023.935548
Minimum0.116484776
Maximum79680.4605
Zeros0
Zeros (%)0.0%
Memory size18.5 KiB
2020-10-24T22:37:50.016125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.116484776
5-th percentile178.4970266
Q1785.794541
median1724.017019
Q33459.936316
95-th percentile9311.095607
Maximum79680.4605
Range79680.34402
Interquartile range (IQR)2674.141775

Descriptive statistics

Standard deviation4677.453811
Coefficient of variation (CV)1.546810022
Kurtosis62.13456502
Mean3023.935548
Median Absolute Deviation (MAD)1115.837176
Skewness6.212280298
Sum7172775.12
Variance21878574.15
MonotocityNot monotonic
2020-10-24T22:37:50.158229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
21.880828281< 0.1%
 
937.67811291< 0.1%
 
1928.9925951< 0.1%
 
7343.4716361< 0.1%
 
2673.0668981< 0.1%
 
1588.3632181< 0.1%
 
1791.2941591< 0.1%
 
19751.659591< 0.1%
 
2586.9968241< 0.1%
 
70.201424091< 0.1%
 
Other values (2362)236299.5%
 
(Missing)20.1%
 
ValueCountFrequency (%) 
0.1164847761< 0.1%
 
0.2485377281< 0.1%
 
0.2492123381< 0.1%
 
1.778806851< 0.1%
 
2.1498536421< 0.1%
 
ValueCountFrequency (%) 
79680.46051< 0.1%
 
58581.835421< 0.1%
 
54184.989911< 0.1%
 
47206.244691< 0.1%
 
43722.717051< 0.1%
 
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
0
2270 
1
 
104
ValueCountFrequency (%) 
0227095.6%
 
11044.4%
 
2020-10-24T22:37:50.254258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
0
2300 
1
 
74
ValueCountFrequency (%) 
0230096.9%
 
1743.1%
 
2020-10-24T22:37:50.293092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
0
1536 
1
838 
ValueCountFrequency (%) 
0153664.7%
 
183835.3%
 
2020-10-24T22:37:50.332485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
1
1355 
0
1019 
ValueCountFrequency (%) 
1135557.1%
 
0101942.9%
 
2020-10-24T22:37:50.371130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
0
2318 
1
 
56
ValueCountFrequency (%) 
0231897.6%
 
1562.4%
 
2020-10-24T22:37:50.414108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
0
2315 
1
 
59
ValueCountFrequency (%) 
0231597.5%
 
1592.5%
 
2020-10-24T22:37:50.452631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
0
2369 
1
 
5
ValueCountFrequency (%) 
0236999.8%
 
150.2%
 
2020-10-24T22:37:50.490939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
1
2251 
0
 
123
ValueCountFrequency (%) 
1225194.8%
 
01235.2%
 
2020-10-24T22:37:50.529237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct7
Distinct (%)0.3%
Missing10
Missing (%)0.4%
Memory size2.7 KiB
Established suburb
829 
Old urban
581 
Urban residential
417 
Patchwork
227 
New development
197 
Other values (2)
113 
ValueCountFrequency (%) 
Established suburb82934.9%
 
Old urban58124.5%
 
Urban residential41717.6%
 
Patchwork2279.6%
 
New development1978.3%
 
Mixed-use753.2%
 
Rural381.6%
 
(Missing)100.4%
 
2020-10-24T22:37:50.621567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T22:37:51.228660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:51.353662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length17
Mean length13.95661331
Min length3

Interactions

2020-10-24T22:36:52.082369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:52.194483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:52.311403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:52.422330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:52.523704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:52.639575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:52.742675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:52.941698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:53.045917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:53.158203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:53.266508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:53.373292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:53.486382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:53.605335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:53.716735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:53.825492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:53.937582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:54.049552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:54.162806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:54.278776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:54.380775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:54.483086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:54.593210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:54.693694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:54.806078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:54.911358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:55.022123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:55.124815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:55.237254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:55.345208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:55.452824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:55.565316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:55.682470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:55.801176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:55.910252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:56.022761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:56.233270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:56.343575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:56.461199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:56.570575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:56.681140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:56.800412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:56.909571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:57.027313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:57.140193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:57.260798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:57.376256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:57.498733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:57.618361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:57.735281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:57.856251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:57.981438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:58.103927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:58.221192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:58.344705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:58.466052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:58.581204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:58.704932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:58.805959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:58.908607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.015583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.115153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.227005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.329027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.445353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.548371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.661074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.767247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.876944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:36:59.988719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:00.105595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:00.343533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:00.455547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:00.568626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:00.679981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:00.787740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:00.906140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:01.022447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:01.138460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:01.265964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:01.385236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:01.511557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:01.628710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:01.754852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:01.874359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:01.999803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:02.120012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:02.240954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:02.365691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:02.495421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:02.624209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:02.746222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:02.871426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:02.996163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:03.117387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-24T22:37:28.940446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:29.064256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:29.186238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:29.320331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:29.440986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:29.560996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:29.987900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:30.106612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:30.231861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:30.347298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:30.476177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:30.593190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:30.716603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:30.835152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:30.954112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:31.081409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:31.210296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:31.336799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:31.458295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:31.584858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:31.708307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:31.828729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:31.955927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:32.067577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:32.178841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:32.297719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:32.415910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:32.536816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:32.648862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:32.769180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:32.881192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:33.002790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:33.118987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:33.233903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:33.356311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:33.481767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:33.605109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:33.723131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:33.844712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:33.965483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:34.084781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:34.211089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:34.330075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:34.449089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:34.576303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:34.694684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:34.822375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:34.941674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:35.069934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:35.191372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:35.319636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:35.443833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:35.568846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:35.699449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:35.832197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:35.962340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:36.089114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:36.218713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:36.349790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:36.475028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-24T22:37:51.502132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-24T22:37:51.916307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-24T22:37:52.325636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-24T22:37:52.768748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-24T22:37:53.272838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-24T22:37:36.863769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:38.531749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:39.229796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T22:37:40.140592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

GEOID10COUNTYFP10Cnty_NameCity_NameZIPnum_bikelanes_Cls0num_bikelanes_Cls1num_bikelanes_Cls2num_bikelanes_Cls3num_bikelanes_Cls4All_lengthssqmiChg_median_rent2000_2015Chg_median_rent1990_2000Chg_nonHisp_whites2000_2015Chg_median_rent1990_2000.1Change _popu_less_highsch2000_2015Change_collg_adultpopu1990_2000Change_collg_adult_popu2000_2015Chg_median_fam_inc1990_2000Chg_median_fam_inc2000_2015Chg_rentburden_fam1990_2000Chg_rentburden_fam2000_2015Chg_median_rent_1990_2000Chg_Sec8_fam2000_2015Racial_Ethnic_Composition_1990Racial_Ethnic_Composition_2000Racial_Ethnic_Composition_2015Disadv_Neighborhds1990_2000Disadv_Neighborhds2000_2015Gentrified_1990_2000Gentrified_2000_2015Disadv_Neighborhds1990_2015Gentrified_1990_2015Job_density_2015_jobs_sqmidisadvantaged_improveddisadvantaged_worseneddisadvantaged_sustained_highdisadvantaged_sustained_lowgentrification_improvedgentrification_worsenedgentrification_sustained_highgentrification_sustained_lowneighbourhood_type
0605906264059OrangeAliso Viejo926560.00.00.00.00.00.00.56919567.064798-0.117756-0.108803-0.0341780.0120750.0115480.0986149601.174436-17960.047070-0.0501010.01790819.7012450.00066901. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN4996.53106500010001Patchwork
1605906264159OrangeAliso Viejo926370.00.00.00.00.00.00.826624183.444294-0.230769-0.0557430.034057-0.001435-0.0833590.187997-16186.632220-3134.2387150.1186470.126962-145.6782500.00805401. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN1106.91210400010001New development
2605906264259OrangeNewport Beach926250.00.00.00.00.00.00.7877021632.381818-0.058285-0.061053-0.001727-0.0299370.1094040.009076-31432.23837028523.531260-0.078095-0.040850136.5632040.00000001. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN445.59970300010001Established suburb
3605906264359OrangeNewport Beach926570.00.00.00.00.00.07.2669851503.374182-0.161147-0.110914-0.019692-0.0091770.1362730.06018471911.94393010177.124730-0.152252-0.019230-413.2414070.00036801. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN249.48448400010001New development
4605906264459OrangeNewport Beach926600.00.00.00.00.00.01.648086425.320158-0.012040-0.051996-0.000331-0.0077060.0669200.056304-11057.537230-24090.471600-0.0178960.199555213.570852-0.00219701. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN640.74326200010001Urban residential
5605906264559OrangeNewport Beach926570.00.00.00.00.00.02.137578288.960474-0.073456-0.091735-0.013762-0.0039700.111063-0.019533-1277.4183734622.276665-0.0289580.073827565.2308000.00000001. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN250.28326500010001New development
6605906264659OrangeLaguna Woods926370.00.00.00.00.00.00.500383-1462.9454550.000581-0.143235-0.013435-0.0868560.0315970.053678-12788.799460-2769.1430320.087822-0.1360901012.0657170.00230901. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN1382.93956200010001Patchwork
7605906264759OrangeLaguna Woods926370.00.00.00.00.00.00.681967-748.075099-0.150992-0.263648-0.024715-0.0094000.0022460.159105-1531.709474-8586.2341640.033927-0.173084749.1953620.00049701. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN8419.76471600010001Mixed-use
8605909925159OrangeFountain Valley927080.00.00.00.00.00.00.511014134.230830-0.211329-0.1379630.045794-0.1105180.0499580.066778-7349.606626-2787.0523090.0542670.023517-90.0378030.03815101. Majority NHW05. No Majority05. No Majority1.01.00.00.0Disadvantaged Both DecadesDisadvantaged, Did Not Gentrify9514.41084900100001Mixed-use
9605909930559OrangeHuntington Beach926480.00.00.00.00.00.00.541894143.122530-0.066012-0.1359880.007648-0.0676120.0403430.017102-1104.818342-5621.601647-0.0778260.112597-156.1342850.00983501. Majority NHW01. Majority NHW01. Majority NHW1.01.00.00.0Disadvantaged Both DecadesDisadvantaged, Did Not Gentrify2860.33800000100001Urban residential

Last rows

GEOID10COUNTYFP10Cnty_NameCity_NameZIPnum_bikelanes_Cls0num_bikelanes_Cls1num_bikelanes_Cls2num_bikelanes_Cls3num_bikelanes_Cls4All_lengthssqmiChg_median_rent2000_2015Chg_median_rent1990_2000Chg_nonHisp_whites2000_2015Chg_median_rent1990_2000.1Change _popu_less_highsch2000_2015Change_collg_adultpopu1990_2000Change_collg_adult_popu2000_2015Chg_median_fam_inc1990_2000Chg_median_fam_inc2000_2015Chg_rentburden_fam1990_2000Chg_rentburden_fam2000_2015Chg_median_rent_1990_2000Chg_Sec8_fam2000_2015Racial_Ethnic_Composition_1990Racial_Ethnic_Composition_2000Racial_Ethnic_Composition_2015Disadv_Neighborhds1990_2000Disadv_Neighborhds2000_2015Gentrified_1990_2000Gentrified_2000_2015Disadv_Neighborhds1990_2015Gentrified_1990_2015Job_density_2015_jobs_sqmidisadvantaged_improveddisadvantaged_worseneddisadvantaged_sustained_highdisadvantaged_sustained_lowgentrification_improvedgentrification_worsenedgentrification_sustained_highgentrification_sustained_lowneighbourhood_type
2364603714390137Los AngelesLos Angeles916040.00.00.0000000.0000000.00.0000002.039747835.419763-0.049363-0.009469-0.0502620.0225910.0848820.065870-3375.931435-20842.6130000.0739000.022133735.3462810.00000001. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN1810.02840100010001Patchwork
2365603718150037Los AngelesLos Angeles900410.00.0663.7576060.0000000.0663.7576060.422678513.868775-0.1152150.004818-0.066647-0.0774770.0411490.094922-4452.50763915871.2370200.024609-0.044068-122.119987-0.00376005. No Majority05. No Majority05. No Majority0.00.00.00.0NaNNaN2219.18549700010001Established suburb
2366603718140037Los AngelesLos Angeles900410.00.02840.8362700.0000000.02840.8362700.339455252.549407-0.178128-0.020040-0.068686-0.1045870.1143750.0676264822.677265-1815.946056-0.0888210.139740-14.360928-0.00343505. No Majority05. No Majority05. No Majority0.00.00.00.0NaNNaN3938.66755200010001Urban residential
2367603718330037Los AngelesLos Angeles900420.00.0925.614921918.6189970.01844.2339180.208999413.384190-0.0806940.0353050.020756-0.236487-0.0301000.106376-6247.8679147638.4406750.089623-0.012084-110.4741340.00557703. Majority Hisp03. Majority Hisp03. Majority Hisp1.01.00.01.0Disadvantaged Both DecadesGentrified 2000-2015 Only1933.02637800100100Urban residential
2368603718351037Los AngelesLos Angeles900420.00.0252.299012441.1125260.0693.4115380.193220298.584980-0.0449480.080321-0.100658-0.1730820.0155640.0864608492.3664242317.508378-0.1850280.194676-96.663303-0.01397503. Majority Hisp03. Majority Hisp03. Majority Hisp1.00.00.00.0Disadvantaged 1990-2000 OnlyDisadvantaged, Did Not Gentrify1060.96567510000001Established suburb
2369603718352037Los AngelesLos Angeles900420.00.0338.5254600.0000000.0338.5254600.165137204.280632-0.0621210.0531700.091877-0.220681-0.0280500.106630-11632.652110-40.470781-0.0425440.126553-172.3589550.00113903. Majority Hisp03. Majority Hisp03. Majority Hisp1.01.00.00.0Disadvantaged Both DecadesDisadvantaged, Did Not Gentrify1810.61346500100001Old urban
2370603743030237Los AngelesMonrovia910160.00.00.0000000.0000000.00.0000000.748014280.430830-0.090984-0.1163860.032373-0.1002110.1052170.120163-584.52714723624.0392300.001530-0.001015-109.216075-0.00034801. Majority NHW01. Majority NHW01. Majority NHW0.00.00.00.0NaNNaN943.83222300010001Established suburb
2371603743072337Los AngelesArcadia910070.00.00.0000000.0000000.00.0000000.187801298.400632-0.298373-0.173704-0.018009-0.0632910.1015190.1400563900.055695-84.084054-0.1370600.106392-113.5417950.00938701. Majority NHW05. No Majority04. Majority Asian0.00.00.00.0NaNNaN2806.16333600010001Old urban
2372603753360337Los AngelesBell902010.00.00.0000000.0000000.00.0000000.312355158.327369-0.023971-0.002144-0.089716-0.0868590.0196370.0359753318.831513-1003.313878-0.0652630.102919-135.9990350.02895203. Majority Hisp03. Majority Hisp03. Majority Hisp1.01.00.00.0Disadvantaged Both DecadesDisadvantaged, Did Not Gentrify8986.57839900100001Old urban
2373603753410137Los AngelesBell Gardens902010.00.00.0000000.0000000.00.0000000.198772184.950988-0.042523-0.008234-0.023687-0.1729680.0265420.0311911502.636123-5023.8315960.0687080.102524-173.2435600.01166303. Majority Hisp03. Majority Hisp03. Majority Hisp1.01.00.00.0Disadvantaged Both DecadesDisadvantaged, Did Not Gentrify12275.37078000100001Patchwork